---
title: MLOps public preview features
description: Read preliminary documentation for MLOps features currently in the DataRobot public preview pipeline.
section_name: MLOps
maturity: public-preview
---

# MLOps public preview features {: #public-preview-features }

{% include 'includes/pub-preview-notice-include.md' %}

## Available MLOps public preview documentation {: #available-mlops-public-preview-documentation }

=== "SaaS"

	Public preview for... | Describes...
	----- | ------
	[Service health and accuracy history](pp-deploy-history) | Service Health and accuracy history allows you to compare the current model and up to five previous models in one place and on the same scale.
	[Timeliness indicators for predictions and actuals](timeliness-status-indicators) | Enable timeliness tracking to retain the last calculated health status and reveal when the status indicators are based on old data.
	[Model logs for model packages](pp-model-pkg-logs) | View model logs for model packages from the Model Registry to see successful operations (INFO status) and errors (ERROR status).
	[Model package artifact creation workflow](pp-model-pkg-artifact-creation) | The improved model package artifact creation workflow provides a clearer and more consistent path to model deployment, with visible connections between a model and its associated model packages.
	[Automated deployment and replacement of Scoring Code in Snowflake](pp-snowflake-sc-deploy-replace) | Create a DataRobot-managed Snowflake prediction environment to deploy and replace DataRobot Scoring Code in Snowflake.
	[Automated deployment and replacement of Scoring Code in AzureML](azureml-sc-deploy-replace) | Create a DataRobot-managed AzureML prediction environment to deploy and replace DataRobot Scoring Code in AzureML.
	[Monitoring jobs for custom metrics](custom-metric-monitoring-jobs) | Monitoring job definitions allow DataRobot to pull calculated custom metric values from outside of DataRobot into the metric defined on the Custom Metrics tab.
	[Remote repository file browser for custom models and tasks](pp-remote-repo-file-browser) | Browse the folders and files in a remote repository to select the files you want to add to a custom model or task.
	[Public network access for custom models](cus-model-pub-network-access) | Access any fully qualified domain name (FQDN) in a public network so that the model can leverage third-party services, or disable public network access to isolate a model from the network and block outgoing traffic.
	[Runtime parameters for custom models](pp-cus-model-runtime-params) | Add runtime parameters to a custom model through the model metadata.
	[MLOps reporting for unstructured models](mlops-unstructured-models) | Report MLOps statistics from custom inference models created with an unstructured regression, binary, or multiclass target type.
	[Versioning support in the Model Registry](model-registry-versioning) | Create registered models to provide an additional layer of organization to your model packages.
	[Compliance documentation with key values](model-registry-key-values) | Build custom compliance documentation templates with references to key values, adding the associated data to the template and limiting the manual editing needed to complete the compliance documentation.
	[MLflow integration for DataRobot](mlflow-integration) | Export a model from MLflow and import it into the DataRobot Model Registry, creating key values from the training parameters, metrics, tags, and artifacts in the MLflow model.
	[Tableau Analytics Extension for deployments](tableau-extension) | Use the Tableau analytics extension to integrate DataRobot predictions into your Tableau project.
	[Multipart upload for the batch prediction API](batch-pred-multipart-upload) | Upload scoring data through multiple files to improve file intake for large datasets.

=== "Self-Managed"

	Public preview for… | Describes...
	----- | ------
	[Service health and accuracy history](pp-deploy-history) | Service Health and Accuracy history allow you to compare the current model and up to five previous models in one place, on the same scale.
	[Timeliness indicators for predictions and actuals](timeliness-status-indicators) | Enable timeliness tracking to retain the last calculated health status and reveal when the status indicators are based on old data.
	[Model logs for model packages](pp-model-pkg-logs) | View model logs for model packages from the Model Registry to see successful operations (INFO status) and errors (ERROR status).
	[Model package artifact creation workflow](pp-model-pkg-artifact-creation) | The improved model package artifact creation workflow provides a clearer and more consistent path to model deployment, with visible connections between a model and its associated model packages.
	[Automated deployment and replacement of Scoring Code in Snowflake](pp-snowflake-sc-deploy-replace) | Create a DataRobot-managed Snowflake prediction environment to deploy and replace DataRobot Scoring Code in Snowflake.
	[Automated deployment and replacement of Scoring Code in AzureML](azureml-sc-deploy-replace) | Create a DataRobot-managed AzureML prediction environment to deploy and replace DataRobot Scoring Code in AzureML.
	[Monitoring jobs for custom metrics](custom-metric-monitoring-jobs) | Monitoring job definitions allow DataRobot to pull calculated custom metric values from outside of DataRobot into the metric defined on the Custom Metrics tab.
	[Remote repository file browser for custom models and tasks](pp-remote-repo-file-browser) | Browse the folders and files in a remote repository to select the files you want to add to a custom model or task.
	[Public network access for custom models](cus-model-pub-network-access) | Access any fully qualified domain name (FQDN) in a public network so that the model can leverage third-party services, or disable public network access to isolate a model from the network and block outgoing traffic.
	[Runtime parameters for custom models](pp-cus-model-runtime-params) | Add runtime parameters to a custom model through the model metadata.
	[Custom model proxy for external models](pp-ext-model-proxy) | (_Self-Managed AI Platform only_) Create custom model proxies for external models in the Custom Model Workshop.
	[MLOps reporting for unstructured models](mlops-unstructured-models) | Report MLOps statistics from custom inference models created with an unstructured regression, binary, or multiclass target type.
	[Versioning support in the Model Registry](model-registry-versioning) | Create registered models to provide an additional layer of organization to your model packages.
	[Compliance documentation with key values](model-registry-key-values) | Build custom compliance documentation templates with references to key values, adding the associated data to the template and limiting the manual editing needed to complete the compliance documentation.
	[MLflow integration for DataRobot](mlflow-integration) | Export a model from MLflow and import it into the DataRobot Model Registry, creating key values from the training parameters, metrics, tags, and artifacts in the MLflow model.
	[Tableau Analytics Extension for deployments](tableau-extension) | Use the Tableau analytics extension to integrate DataRobot predictions into your Tableau project.
	[Multipart upload for the batch prediction API](batch-pred-multipart-upload) | Upload scoring data through multiple files to improve file intake for large datasets.